The errors sources can be separated into three groups: rounding, data uncertainty, and truncation. [1]}
Rounding errors are unavoidable due to the finite precision.
Data uncertainty comes from the initial input or the result of a previous computation.
Because of the limitation of technology, instrument or approach,
input data from measurements or estimations is usually inaccurate to some extent.
Truncation arises when a numerical algorithm approximates a mathematical function.
Such as a simulation algorithm of Taylor Expansion.
Furthermore, due to the approximation, floating-point arithmetic is prone to accuracy problems
caused by accumulation and catastrophic cancellation.
